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test_C2FNet.py
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test_C2FNet.py
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import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import torch
import torch.nn.functional as F
import numpy as np
from utils import data_loader
from tqdm import tqdm
from utils.metrics import Evaluator
from PIL import Image
from network.SemiModel import SemiModel
import time
start=time.time()
def test(test_loader, Eva_test, save_path, net):
print("Strat validing!")
net.train(False)
net.eval()
for i, (A, B, mask, filename) in enumerate(tqdm(test_loader)):
with torch.no_grad():
A = A.cuda()
B = B.cuda()
Y = mask.cuda()
preds = net(A,B)
output = F.sigmoid(preds[1])
output[output >= 0.5] = 1
output[output < 0.5] = 0
pred = output.data.cpu().numpy().astype(int)
target = Y.cpu().numpy()
for i in range(output.shape[0]):
probs_array = (torch.squeeze(output[i])).data.cpu().numpy()
final_mask = probs_array * 255
final_mask = final_mask.astype(np.uint8)
final_savepath = save_path + filename[i] + '.png'
im = Image.fromarray(final_mask)
im.save(final_savepath)
Eva_test.add_batch(target, pred)
print('target.shape', target.shape)
print('pred.shape', pred.shape)
IoU = Eva_test.Intersection_over_Union()
Pre = Eva_test.Precision()
Recall = Eva_test.Recall()
F1 = Eva_test.F1()
OA=Eva_test.OA()
Kappa=Eva_test.Kappa()
# print('[Test] IoU: %.4f, Precision:%.4f, Recall: %.4f, F1: %.4f' % (IoU[1], Pre[1], Recall[1], F1[1]))
print('[Test] F1: %.4f, Precision:%.4f, Recall: %.4f, OA: %.4f, Kappa: %.4f,IoU: %.4f' % ( F1[1],Pre[1],Recall[1],OA[1],Kappa[1],IoU[1]))
# print('F1-Score: {:.2f}\nPrecision: {:.2f}\nRecall: {:.2f}\nOA: {:.2f}\nKappa: {:.2f}\nIoU: {:.2f}\n}'.format(F1[1] * 100, Pre[1] * 100, Recall[1] * 100, OA[1] * 100, Kappa[1] * 100, IoU[1] * 100))
print('F1-Score: Precision: Recall: OA: Kappa: IoU: ')
# print('{:.2f}\{:.2f}\{:.2f}\{:.2f}\{:.2f}\{:.2f}'.format(F1[1] * 100, Pre[1] * 100, Recall[1] * 100, OA[1] * 100, Kappa[1] * 100,IoU[1] * 100))
print('{:.2f} {:.2f} {:.2f} {:.2f} {:.2f} {:.2f}\n'.format(F1[1] * 100, Pre[1] * 100, Recall[1] * 100, OA[1] * 100, Kappa[1] * 100,IoU[1] * 100))
# print('{:.2f} {:.2f} {:.2f} {:.2f} {:.2f} {:.2f}\n'.format(F1[0] * 100, Pre[0] * 100, Recall[0] * 100, OA[0] * 100, Kappa[0] * 100,IoU[0] * 100))
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batchsize', type=int, default=16, help='training batch size')
parser.add_argument('--trainsize', type=int, default=256, help='training dataset size')
parser.add_argument('--gpu_id', type=str, default='1', help='train use gpu') #修改这里！！！
parser.add_argument('--data_name', type=str, default='WHU', #修改这里！！！
help='the test rgb images root')
parser.add_argument('--model_name', type=str, default='SemiModel_noema04', #修改这里！！！
help='the test rgb images root')
# parser.add_argument('--save_path', type=str, default='./test_result/C2F-SemiCD/WHU/WHU-5-Student/') #半监督C2F-SemiCD影像保存路径！！！
parser.add_argument('--save_path', type=str, default='./test_result/C2FNet/WHU/WHU-5-Student/') #全监督C2FNet影像保存路径！！
opt = parser.parse_args()
# set the device for training
if opt.gpu_id == '0':
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print('USE GPU 0')
elif opt.gpu_id == '1':
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
print('USE GPU 1')
if opt.gpu_id == '2':
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
print('USE GPU 2')
if opt.gpu_id == '3':
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
print('USE GPU 3')
if opt.data_name == 'LEVIR':
opt.test_root = '/data/chengxi.han/data/LEVIR CD Dataset256/test/'
# opt.test_root = '/data/chengxi.han/data/LEVIR-CD-Dataset256/test256-sub/'
# opt.test_root = '/data/chengxi.han/data/LEVIR CD Dataset256/test256-sub-331/'
# opt.test_root = '/data/chengxi.han/data/LEVIR-CD-Dataset256/test256-sub-21/'
# opt.test_root = '/data/chengxi.han/data/LEVIR-CD-Dataset256/test256-sub-1419/'
elif opt.data_name == 'WHU':
opt.test_root = '/data/chengxi.han/data/Building change detection dataset256/test/'
# opt.test_root = '/data/chengxi.han/data/WHU-CD-256-Semi/test/'
# opt.test_root = '/data/chengxi.han/data/Building change detection dataset256/test-sub/'
# opt.test_root = '/data/chengxi.han/data/Building change detection dataset256/test-sub-61-3/'
elif opt.data_name == 'CDD':
opt.test_root = '/data/chengxi.han/data/CDD_ChangeDetectionDataset/Real/subset/test/'
# opt.test_root = '/data/chengxi.han/data/Building change detection dataset256/test-sub-CDD402/'
elif opt.data_name == 'DSIFN':
opt.test_root = '/data/chengxi.han/data/DSIFN256/test/'
elif opt.data_name == 'SYSU':
opt.test_root = '/data/chengxi.han/data/SYSU-CD/test/'
# opt.test_root = '/data/chengxi.han/data/SYSU-CD/test-sub/'
elif opt.data_name == 'S2Looking':
opt.test_root = '/data/chengxi.han/data/S2Looking256/test/'
# opt.test_root = '/data/chengxi.han/data/S2Looking256/test-sub/'
elif opt.data_name == 'GoogleGZ':
opt.test_root = '/data/chengxi.han/data/Google_GZ_CD256/test/'
elif opt.data_name == 'LEVIRsup-WHUunsup':
opt.test_root = '/data/chengxi.han/data/WHU-LEVIR-CD-256-Semi/test/'
opt.save_path = opt.save_path + opt.data_name + '/' + opt.model_name + '/'
test_loader = data_loader.get_test_loader(opt.test_root, opt.batchsize, opt.trainsize, num_workers=2, shuffle=False, pin_memory=True)
Eva_test = Evaluator(num_class=2)
if opt.model_name == 'HANet_v2':
model = HANet_v2().cuda()
elif opt.model_name == 'SemiModel_noema04':
model = SemiModel().cuda()
#-------测试模型teacher和student中任选一个！-------
#-------测试老师的模型teacher-------
# save_path = './output/C2F-SemiCD/WHU-5/' #半监督C2F-SemiCD模型路径！！
# save_path = './output/C2FNet-WHU/' #全监督C2FNet模型路径！！
# # save_path = save_path + '/' + opt.model_name
# save_path = save_path + opt.data_name + '/' + opt.model_name
# opt.load = save_path + '_train1_' + '_best_teacher_iou.pth'
# # opt.load = save_path + '_train1_'+ '_best_student_iou.pth'
# if opt.load is not None:
# model.load_state_dict(torch.load(opt.load))
# print('load model from ', opt.load)
# # print('路径是：WHU-labeled-5-semiloss-0.8')
#-------测试学生的模型student-------
# save_path = './output/C2F-SemiCD/WHU-5/' #半监督C2F-SemiCD模型路径！！
save_path = './output/C2FNet/WHU/' #全监督C2FNet模型路径！！
# save_path = save_path + opt.model_name
save_path = save_path + opt.data_name + '/' + opt.model_name
# opt.load = save_path + opt.data_name + '/' + opt.model_name+ '_train1_' + '_best_student_iou.pth'
# save_path = save_path + opt.model_name
opt.load = save_path + '_train1_'+ '_best_student_iou.pth'
# opt.load ='./output/LEVIR-5%/SemiModel_noema04_best_teacher_iou.pth'
if opt.load is not None:
print('load model from ', opt.load)
checkpoint_stud = torch.load(opt.load)
model.load_state_dict(checkpoint_stud['best_student_net '])
#-------测试模型teacher和student中任选一个！-------
save_path = opt.save_path
if not os.path.exists(save_path):
os.makedirs(save_path)
test(test_loader, Eva_test, opt.save_path, model)
end=time.time()
print('程序测试test的时间为:',end-start)